Patient involvement in research has been highlighted as a major requirement for the development of products and services that cover actual patients' needs. However, there has not been an agreement on a commonly used standard for patient involvement in research, at least not in the EU, partially because of lack of common terminology and implementation methodology. Within the standardization activities of "LifeChamps: A Collective Intelligent Platform To Support Cancer Champions", this qualitative study was developed to discover patients' views for their engagement in research. This is an ongoing qualitative study of semi-structured interviews of cancer survivors aged over 65 years of age, exiting the feasibility studies of the LifeChamps project in Stockholm and Thessaloniki. Findings from the thematic analysis of this study are expected to indicate requirements for involvement of patients in research studies as participants.
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http://dx.doi.org/10.3233/SHTI230729 | DOI Listing |
RMD Open
December 2024
Department of Gastroenterology, Infectious Diseases and Rheumatology (incl. Nutrition Medicine), Charite - Universitatsmedizin Berlin, Berlin, Germany.
Purpose: To examine whether incorporating anatomy-centred deep learning can improve generalisability and enable prediction of disease progression.
Methods: This retrospective multicentre study included conventional pelvic radiographs of four different patient cohorts focusing on axial spondyloarthritis collected at university and community hospitals. The first cohort, which consisted of 1483 radiographs, was split into training (n=1261) and validation (n=222) sets.
Radiology
October 2024
From the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 149 Thirteenth St, Charlestown, MA 02129 (F.J.D., T.R.B., M.C.C., A.E.K., C.P.B.); Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany (F.J.D., L.D., F.A.M., F.B., L.J.); Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Mass (L.J.); Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany (L.C.A.); Mass General Brigham Data Science Office, Boston, Mass (J.S., T.S., C.P.B.); Microsoft Health and Life Sciences (HLS), Redmond, Wash (J.M.); Klinikum rechts der Isar, Technical University of Munich, Munich, Germany (K.K.B.); Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany (K.K.B.); and Department of Cardiovascular Radiology and Nuclear Medicine, Technical University of Munich, School of Medicine and Health, German Heart Center, TUM University Hospital, Munich, Germany (K.K.B.).
BMC Med Educ
September 2024
School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Background: The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional differences in these perceptions?
Methods: An anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students' AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching.
Radiol Artif Intell
September 2024
From the Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany (L.C.A., K.K.B.); and Institute for Radiology and Nuclear Medicine, German Heart Center Munich, Technical University of Munich, Munich, Germany (K.K.B.).
Radiol Artif Intell
September 2024
From the Department of Radiology (F.B., L.H., S.M.N.), Department of Anesthesiology, Division of Operative Intensive Care Medicine (F.B.), Department of Cardiology (P.S.), and Department of Rheumatology (K.B.B., D.P., A.Z.), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 12203 Berlin, Germany; Department of Radiology and Nuclear Medicine, German Heart Center, Technical University of Munich, Munich, Germany (K.K.B.); Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany (F.B., K.K.B., M.R.M., L.C.A.); Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Departments of Radiation Oncology and Radiology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, Mass (H.J.W.L.A.); and Department of Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.).
Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiographs. Materials and Methods This institutional review board-approved retrospective study included patients with implantable pacemakers, cardioverter defibrillators, cardiac resynchronization therapy devices, and cardiac monitors who underwent chest radiography between January 2012 and January 2022. A U-Net model with a ResNet-50 backbone was created to classify CIEDs on DICOM and smartphone images.
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